Tissue micro-arrays are multiple specimen slides that contain hundreds of individual tissues for one or multiple different biological specimens. TMA allows staining (e.g., with Haematoxylin and Eosin (H/E) stain, etc.) and analysis of hundreds of samples on a slide over traditional one per slide. Tissues from multiple patients or blocks are relocated from conventional histologic paraffin blocks on the same slide. This is done by using a needle to biopsy a standard histologic sections and placing the core into an array on a recipient paraffin block. This technique was originally described by in 1987 by Wan, Fortuna and Furmanski in Journal of Immunological Methods. They prepared of “cores” of paraffin-embedded tissue from standard histology blocks. The paraffin embedded cores of the tissue were straightened, inserted into a casing and mounted in a paraffin block and sectioned. Over 120 tissue samples were analysed. Olli Kallioniemi and Juha Kononen in 1998 developed an ordered array of tissue cores, up to 1,000 of them, on a single glass slide termed tissue micro-array (TMA) and publishing it in the journal Nature Medicine, thereby validating the technique.
TMA technology allows rapid visualization of molecular targets in thousands of tissue specimens at a time, at the DNA, RNA, protein levels, etc. Moreover, this technique requires less tissue for analysis and offers consistency in reporting results. Additionally with serial sections of the master block, investigators can analyze numerous biomarkers over essentially identical samples. Configuration of TMA depends on the end use. There could be samples of every organ in a particular animal's or human's body, or a variety of common cancers like breast and colon carcinomas with normal controls, or rare or obscure cases, such as an array of salivary gland tumors. An array of tissues from different knockout mice or a single, specific tissue (e.g., from cultured cells) could also be assayed. These slides with TMAs are treated like other individual histological section, using in situ hybridization to detect gene expression or identify chromosomal abnormalities, or employing immunohistochemistry (IHC) to localize protein expression.
More broadly, researchers use TMAs to validate potential drug targets identified with DNA TMAs. Scientists typically construct TMAs in paraffin blocks. Each tissue core in the array is collected as a “punch” generally 0.6 millimeters (mm) to 2.0 mm in diameter, at a spacing of about 0.7 mm to 0.8 mm from a donor block of paraffin-embedded tissue, using a needle. The surface area of each sample is about 0.282 mm2, or in pathologists' terms, about the size of 2-3 high power fields. A second, slightly smaller needle is used to create a hole in the recipient block. The tissue cores are then arrayed in the recipient block to produce a master block, from which researchers can obtain around 200 individual 5 micrometer (μm) slices.
Most of the applications of the TMA technology have conic from the field of cancer research. Examples include analysis of the frequency of molecular alterations in large tumor materials, exploration of tumor progression, and identification of predictive or prognostic factors and validation of newly discovered genes as diagnostic and therapeutic targets. A standard histologic section is about 3-5 mm thick, with variation depending on the submitting pathologist or tech. After use for primary diagnosis, the sections can be cut 50-100 times depending on the care and skill of the sectioning technician. Thus, on average, each archived block might yield material for a maximum of 200 assays. If this same block is processed for optimal TMA construction it could routinely be needle biopsied 200-300 times or more depending on the size of the tumor in the original block (Theoretically it could be biopsied 1000's of times based on calculations of area, but empirically, 200-300 is selected as a conservative estimation).
Once TMAs are constructed, they can be judiciously sectioned in order to maximize the number of sections cut from an array. The sectioning process uses a tape-based sectioning aid that allows cutting of thinner sections. Optimal sectioning of arrays is obtained with about 2-3 μm sections. Thus, instead of 50-100 conventional sections or samples for analysis from one tissue biopsy, TMA techniques produce material for 500,000 assays (assuming 250 biopsies per section times 2000 2.5 μm sections per 5 mm array block) represented as 0.6 mm disks of tissue. TMA techniques essentially amplifies (up to 10,000 fold) from a limited tissue resource.
Another significant advantage is that only a very small (a few microliter (μl)) amount of reagent is required to analyze an entire TMA. This advantage raises the possibility of use of TMA in screening procedures (for example in hybridoma screening), a protocol that is impossible using conventional sections. TMAs also save money when reagents are costly. Finally, there are occasions where the original block of tissue must be returned to the patient or donating institution. In these cases the tissue block may be cored a few times without destroying the block. Then upon subsequent sectioning, it is still possible to make a diagnosis because tissue has been taken for TMA-based studies. Ultimately, this type of research helps clinicians make better diagnoses and better decisions about patient care.
Rapidly advancing technology has created exciting opportunities for researchers and physicians, who are trying to elucidate the causes of disease, create predictive or diagnostic assays and develop effective therapeutic treatments. Large-scale and high-throughput genomic and proteomic studies are generating vast amounts of data that are already leading to the identification of drug targets and disease biomarkers. The new challenge is to sift through all of the gene and protein expression data to find clinically relevant information. A rate-limiting step in the screening process has been the need to examine histological samples one at a time. This degree of scrutiny is necessary to interpret the often-complex expression and distribution patterns of target molecules within actual tissues. The reproducibility in TMA is achieved by large numbers essential for the statistically significant detection of biomarkers, protein and gene expression.
TMA applications include studies that attempt to link gene expression data with stages of tumor progression, screening and validation of drug targets, and quality control for molecular detection methods. Example applications of tissue micro-arrays in cancer research including analyzing the frequency of a molecular alteration in different tumor types, to evaluate prognostic markers, to test potential diagnostic markers and optimize antibody-staining conditions.
According to a recent survey, over 40% of researchers who currently use TMA are working on cancer research or diagnosis. Since tissue micro-arrays, per se, were developed by researchers at the National Cancer Institute, it is not surprising that early adopters of this technology are using them in oncology. Future market growth will be driven by adoption of tissue micro-arrays in other areas of research, such as neurobiology and infectious disease, as well as their increased utilization in high-throughput analysis of tissue sections, validation of DNA micro-array data and biomarker discovery.
Two recent studies highlight this point. Yale researchers recently published a study on HER2 expression in breast cancer tissue using TMAs, in which they found that higher levels of HER2 protein correlated with poorer clinical outcomes. The research used 300 archived tissue specimens, which were taken from patients diagnosed with invasive breast carcinoma from 1962 to 1977. But the scientists took just two 0.6-mm diameter cores from each sample, thereby preserving the archived tissues for future studies. In an earlier report, the same team studied the prognostic value of beta-catenin expression in 310 colon carcinoma specimens collected between 1971 and 1982.
The latter Yale study illustrates another benefit of TMA technology: quantitative analysis. Traditionally, pathologists use a four-point scale to rate specimens. Having a pathologist score each specimen is not only slow and laborious, but also yields results that are subjective, difficult to reproduce, and that don't reflect subtleties.
Therefore, the advantages of TMA analysis are speed, throughput, and standardization, ease-of-use, conservation of valuable tissue samples.
However, there are several problems associated with existing TMA technologies. The TMA techniques require complicated data collection and management has resulted in huge data which researchers are only now beginning to address. The usage of this technology has gained popularity and is being used more and more. TMA users must keep track of both clinical and experimental data. Each new biomarker studied in a given array increases the data's complexity. TMA is an informatics challenge. A software system for image archiving allowing a user to examine digital images of individual histological specimens, such as tissue cores from a TMA; evaluate and score them; and store all the data in a relational database is essential for TMA.
Tissue scoring is inherently subjective and imprecise. It is nonquantitative based on a manual score using a four-point scale: negative, weak: positive, strong positive, or no data. It calls for an automated image analysis process that can localize and quantify the biomarkers in the given set of array. It can assist pathologist in more objective analysis. Quantitative measurements ultimately will allow predictions about patient outcomes and their response to therapy. But for most, the promise of TMAs remains unfulfilled, because scientists lack user friendly methods of high-speed automated quantitative.
About 90% of all human cancers are of epithelial origin. Diagnosis and prognosis of the epithelial tumors by pathologist involves microscopic analysis of the tissue. The expertise of the pathologist immediately allows him/her to identify an epithelial region in a given field against a stromal region to further characterize it. Thus identification and quantification of epithelial and stromal area of a given digital image of a tumor tissue is the first step in the analysis.
There is typically an interobserver and an intraobserver variability and lack of reproducibility in identifying specific morphological features manually by pathologists. This variability is partly due to the inherent difficulty of the specialty to varying levels of expertise among pathologists and to differences in subjective analysis and comprehension of pathological images.
Quantification of epithelial area with TMAs using an automated method offers practical advantages. Identification of the epithelium provides additional information for discrimination between the borderline and the malignant tumors. It can be done through measurement of the area percentage of the epithelium in tissue sections. Automated identification of epithelial area that can imitate basic processes of human visual image perception by computation of staining properties and generate results as per the requirement will assist pathologist in reducing the subjectivity in the field.
A number of companies have developed a variety of hardware and software solutions for TMA analysis. For example, Bacus Laboratories' BLISS system uses a tiling approach that scans a TMA piecemeal and then stitches together all the tiles to produce a single composite image.
Aperio Technologies' ScanScope digitizes an entire TMA array slide by applying linear detector technology used in fax machines. Trestle, with its MedScan product employs area scanning Applied Imaging's Ariol imaging and analysis system can image both colorimetric and fluorescently labeled samples.
Beecher Instruments is producing a TMA analysis package based upon contextual information rather than pixel information. TissueAnalytics Arrayf(x) the software from Tissueinformatics Inc., gives information about the subcellular location of staining and can detect the presence of rare events, proteins expressed at low levels.
Mark Rubin, associate professor of pathology at Brigham & Women's Hospital, Harvard Medical School, helped develop a software system to deal with the image archiving problem while he was an associate professor at the University of Michigan. The software, called Profiler (portal.path.med.umich.edu), allows researchers to examine digital images of individual histological specimens, such as tissue cores from a TMA; evaluate and score them; and store all the data in a relational database.
Chih Long Liu, working with Mat van de Rijn developed a solution to some TMA bookkeeping headaches with two programs: TMA-Deconvoluter and Stainfinder (genome-www.Stanford.edulTMA/). TMA-Deconvoluter is a series of Excel macros that helps researchers get TMA data into a format that can be read by conventional data analysis tools like Cluster and TreeView (rana.lbl.gov). Cluster runs a hierarchical cluster analysis on the TMA data, helping users to interpret the highly complex datasets obtained from TMAs stained with large numbers of antibodies, and TreeView allows researchers to browse the clustered data. Stainfinder is a Web interface that links the clustered TMA data to an online image database, allowing scientists to rapidly reevaluate the data and compare different stains on the same core.
It is observed that none of the methods in prior art provides a comprehensive solution to automated high speed TMA analysis addressing the issues of reliable automatic gridding and TMA core boundary detection. These issues and other issues need to be overcome, especially if solution needs to accommodate overlapping TMA cores.
Therefore it is desirable to provide a method and system for automated quantitation of tissue micro-array (TMA) digital image analysis.